Big Data Analytics: From SQL to Hadoop and Beyond

by Nicolas Spyratos

University of Paris SouthFrance

Abstract

We present in this talk a high level query language, called HiFun, for defining analytic queries over big data sets. An analytic query in HiFun is defined to be a well-formed expression of a functional algebra, whose operations combine functions to create HiFun queries (in much the same way as the operations of the relational algebra combine relations to create relational algebra queries. We show that a HiFun query can be encoded as a map-reduce job, and also as a SQL group-by query when the data set resides in a relational database. We also present a formal method for rewriting HiFun queries and defining query execution plans. As a case study, we show how the rewriting method for HiFun queries can be applied in the rewriting of map-reduce jobs and SQL group-by queries.

Biography

Nicolas Spyratosis currently professor emeritus at the University of Paris South, scientific advisor of the Japan Science and Technology agency (JST), member of the Greek National Council of Research and Innovation and Adjunct Senior Researcher at the FORTH Institute of Computer Science in Greece. His research interests include databases, big data analytics, digital libraries and conceptual modeling. He has published over 200 papers in refereed international journals and conferences and has participated in over 20 European and international research projects. He has supervised 24 doctoral theses and has been evaluator for the European programs Esprit and Esprit-Bra as well as for the National Science Foundation (NSF) and major scientific journals.

Use of Data Analytics & Computational Intelligence for Services Computing

by Marouane Kessentini

University of Michigan-DearbornUSA

Abstract

A growing trend has begun in recent years to move software engineering problems from human-based search to machine-based search that balances a number of constraints to achieve optimal or near-optimal solutions. As a result, human effort is moving up the abstraction chain to focus on guiding the automated search, rather than performing the search itself. This emerging software engineering paradigm is known as Search Based Software Engineering (SBSE). It uses data analytics and computational intelligence techniques, mainly those from the evolutionary computation literature to automate the search for optimal or near-optimal solutions to software engineering problems. The SBSE approach can and has been applied to many problems in software engineering that span the spectrum of activities from requirements to maintenance and reengineering. Already success has been achieved in requirements, refactoring, project planning, testing, maintenance and reverse engineering. However, several challenges have to be addressed to mainly tackle the growing complexity of software systems nowadays in terms of number of objectives, large amount of data (history of changes and commits), constraints and inputs/outputs. In this talk, I will give, first, an overview about SBSE then I will focus on some contributions that I proposed, along with my research group and my industrial partners, addressing the above challenges in the area of services computing, including: Web services quality, services composition, Services refactoring, etc. Finally, I will discuss possible new research directions in SBSE.

Biography

Marouane Kessentini is an Assistant Professor in the Department of Computer and Information Science at the University of Michigan, Dearborn, MI. He is the founder of the Search-Based Software Engineering (SBSE) research lab. He is recognized by many research surveys published in various venues as a leading research in the areas of SBSE and software refactoring. Dr. Kessentini has several collaborations with different industrial companies on the use computational search, machine learning and evolutionary algorithms to address several software engineering and services computing problems such as software quality, software testing, software migration, software evolution, services quality, services composition, services refactoring, etc. He received a best PhD award from University of Montreal in 2012 and a Presidential BSc Award from the President of Tunisia in 2007. He received many grants from both industry and federal agencies and published over 100 papers in software engineering, services computing and computational intelligence journals and conferences, including 3 best paper awards. He has served as program committee member in over 100 major conferences (GECCO, ASE, MODELS, ICWS, ICSOC, ICMT, SSBSE, etc.), an editorial board member of several journals, and an organization member of many conferences and workshops. He was also the co-chair of the SBSE track at the GECCO2014 and GECCO2015 conferences and he was the general chair of the 8th Search Based Software Engineering Symposium (SSBSE2016). He is also the founder of the North American Symposium on Search Based Software Engineering, funded by the National Science Foundation (NSF) and an invited speaker at the 2016 IEEE World Congress on Computational Intelligence (Vancouver, Canada).

by Peter L. Stanchev

Kettering UniversityUSA

Abstract

The world’s digital content and media is growing rapidly at a never stopping rate. There are millions of digital media assets on display through mobile devices, home entertainment systems or computers. The vast pool of visual and audio information has to therefore be grouped in different ecosystems depending on their nature or intended audience to simplify the problem of searching, finding and personalizing datasets on demand. Though such is the case for the Digital Cultural Ecosystems, we still need to introduce number of smart methodologies to make the process of narrowing down vast number of digital assets in order to arrive at a desirable media and essentially personalize and automate the approach. In this talk, we propose a method that deals with the detection, extraction and personalization of media assets applied to the world of digital cultural ecosystems.

Biography

Peter Stanchev is a professor at the Software Engineering and Information Systems Department at the Institute of Mathematics and Informatics, Bulgarian Academy of Sciences. He has forty years of professional experience in of multimedia systems, database systems, multimedia semantics, education, open access to scientific information and data and medical systems. He is also a professor at Kettering University, Flint, Michigan, USA. He has M.Sc., Ph.D. and D.Sc. in Mathematics/Computer Science from Sofia University. He has published 2 books, more than 200 chapters in monographs, journal and conference peer-reviewed papers, more than 200 conference papers and seminars, and has had more than 1700 citations, h-index - 36, impact factor – 77.03. Serving also on many database and multimedia conference program committees, he is currently editor-in-chief and member of the editorial boards of several journals. He is the Bulgarian representative in the EU OpenAIRE projects.

by Kokou Yetongnon

University of Dijon - Franche ComteFrance

Abstract

Interoperability is the ability of systems to enable information or knowledge sharing through exchange of requests for data or services based on mutual understanding. Early work on information system interoperability were based on 1) determining and resolving semantic discrepancies among information schemas and 2) providing semantic based methods or tools to align and integrate schema components. In this talk, we will focus on and highlight the important progress that have been achieved in this research area. This progress has enabled many evolving trends and state of the art solutions, supported by semantic tools such as service architectures, ontologies, semantic web and linked data etc., to carry out automatic discovery, matching and fusion of data from diverse heterogeneous sources. Despite this progress, a number of challenging issues must be addressed in order to achieve the full potential of information system interoperability. We will present some of the challenges and analyze the solutions proposed to overcome them. One of the important challenges aims to address security and privacy issues in data interoperation. We will discuss development and architectures issues in fields such as e-government and smart data platforms. Finally, interoperability and its impacts on big data analytics and architectures will be discussed.

Biography

Dr Kokou Yetongnon is a Professor in the Departement of Computer Science at the University of Bourgogne in Dijon, France. From 1979 to 1983 he was a graduate Research and Teaching assistant in the Department of Computer & Engineering, University of Connecticut. In 1984 he was appointed as an Adjunct Assistant Professor in Computer & Engineering, University of Connecticut. He held appointments as visiting scholar at many Universities including, research assistant at Ecole Polytechnique Federale Lausanne, Switzerland, Professor of Computer Sciences at the Cadi Ayad University in Marrakesh, the University of Abomey Calavi in Benin and the University of Benin in Togo. In 2001 on sabbatical leave, he was appointed as a Visiting Professor in Computer Sciences at the University of Connecticut, USA. He received the BS degree in Mathematics (University of Lomé, Togo), English certificate from Georgetown University, USA, the M.sc and PhD in Computer Sciences from the University of Connecticut, USA. He is the author of several conference and journal papers interoperable systems, distributed computing and performance of distributed information systems. His list of publication is available on Google Scholar and Research Gate. He is the member of program committee of several conferences special issues of journals. He is Guest Editor of several special issues of journals. He is the Chair of the Steering Committee of the SITIS conference series.